Meta-RL Gradient Estimation with Control Variates
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Solution Overview
Problem
Deep reinforcement learning neural networks face high sample complexity and fail to adapt efficiently to new tasks, leading to poor sample efficiency and unstable training due to high variance in gradient estimation.
Innovation Solution
Incorporating control variates into gradient estimation via automatic differentiation and extending the meta-learning framework to learn control variates, enabling efficient learning across a distribution of Markov decision processes for related tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning is used for task learning, then the model can handle complex tasks including game playing and natural language processing, but the sample complexity becomes excessively high and the model fails to adapt to new situations
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on a distribution of related tasks before deployment. This meta-training phase prepares the model with generalized knowledge that enables rapid adaptation to new tasks within the same distribution, reducing the samples needed for each new task.
Solution Approach 2:
The patent implements universality by designing a single neural network model that can handle multiple related tasks within a task distribution. The model learns task-agnostic representations and adaptation mechanisms that allow it to function across different tasks (e.g., different games or NLP tasks) without task-specific retraining.
2Reliability
If standard gradient estimation is used in meta-reinforcement learning, then the training process can proceed, but the high variance in gradient estimation leads to unstable training and poor sample efficiency
Solution Approach 1:
The patent introduces control variates as intermediary components in the gradient estimation process. These control variates serve as mediators that reduce the variance of gradient estimates by subtracting out high-variance components, leading to more stable and sample-efficient training without introducing bias.
Solution Approach 2:
The patent implements feedback mechanisms where gradient estimates from previous iterations are used to adjust and improve subsequent gradient estimates. This feedback loop allows the system to learn from past gradient variations and progressively reduce variance, improving both training stability and sample efficiency.
Data Source
AI summary
A method for deep reinforcement learning using a neural network model includes receiving a distribution including a plurality of related tasks. Parameters for the reinforcement learning neural network model is trained based on gradient estimation associated with the parameters using samples associated with the plurality of related tasks. Control variates are incorporated into the gradient estimation by automatic differentiation.